• 제목/요약/키워드: fuzzy modeling

검색결과 736건 처리시간 0.029초

이산 시간 비선형 상호 결합 시스템의 T-S 퍼지 모델을 위한 분산 동적 출력 궤한 제어기 설계 (Decentralized Dynamic Output Feedback Controller for Discrete-time Nonlinear Interconnected Systems via T-S Fuzzy Models)

  • 구근범;김진규;주영훈;박진배
    • 한국지능시스템학회논문지
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    • 제17권6호
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    • pp.780-785
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    • 2007
  • 본 논문은 Takagi-Sugeno (T-S) 퍼지 모델을 이용하여 이산 시간에서의 비선형 상호 결합 시스템에 대한 분산 동적 출력제한 제어기를 제시한다. 이산시간 비선형 상호 결합 시스템의 각 하위 시스템에 대한 T-S 퍼지 모델링을 한 후, 각각에 대해 동적 출력 궤한 제어기를 설계한다. 제어가 된 폐루프 하위 시스템들로 전체 시스템의 평형점이 안정화되는 선형 행렬 부등식 (LMI)을 구하고, 부등식을 이용하여 동적 출력 제한 제어기의 이득 값을 구한다. 마지막으로 모의실험을 통해 분산 동적 출력 궤한 제어기의 효용성을 확인한다.

종속형 퍼지-뉴럴 네트워크를 이용한 풍력발전기 출력 예측 (Estimation of Wind Turbine Power Generation using Cascade Architectures of Fuzzy-Neural Networks)

  • 김성민;이동훈;장종인;원정철;강태호;임영근;한창욱
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2009년도 제40회 하계학술대회
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    • pp.1098_1099
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    • 2009
  • In this paper, we present the estimation of wind turbine power generation using Cascade Architectures of Fuzzy Neural Networks(CAFNN). The proposed model uses the wind speed average, the standard deviation and the past output power as input data. The CAFNN identification process uses a 10-min average wind speed with its standard deviation. The method for rule-based fuzzy modeling uses Gaussian membership function. It has three fuzzy variables with three modifiable parameters. The CAFNN's configuration has three Logic Processors(LP) that are constructed cascade architecture and an effective optimization method uses two-level genetic algorithm. First, The CAFNN is trained with one-day average input variables. Once the CAFNN has been trained, test data are used without any update. The main advantage of using CAFNN is having simple structure of system with many input variables. Therefore, The proposed CAFNN technique is useful to predict the wind turbine(WT) power effectively and hence that information will be helpful to decide the control strategy for the WT system operation and application.

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퍼지제어기를 이용한 차동브레이크 시스템의 능동 조향제어 (Active Handling Control of the Differential Brake System Using Fuzzy Controller)

  • 윤여흥;장봉춘;이성철
    • 한국정밀공학회지
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    • 제20권5호
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    • pp.82-91
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    • 2003
  • Vehicle dynamics control (VDC) has been a breakthrough and become a new terminology for the safety of a driver and improvement of vehicle handling. This paper examines the usefulness of a brake steer system (BSS), which uses differential brake forces for steering intervention in the context of VDC, In order to help the car to turn, a yaw moment can be achieved by altering the left/right and front/rear brake distribution. The steering function achieved through BSS can then be used to control lateral position in an unintended road departure system. An 8-DOF non-linear vehicle model including STI tire model will be validated using the equations of motion of the vehicle, and the non-linear vehicle dynamics. Since fuzzy logic can consider the nonlinear effect of vehicle modeling, fuzzy controller is designed to explore BSS feasibility, by modifying the brake distribution through the control of the yaw rate of the vehicle. The control strategies developed will be tested by simulation of a variety of situation; the possibility of VDC using BSS is verified in this paper.

Solving a New Multi-Period Multi-Objective Multi-Product Aggregate Production Planning Problem Using Fuzzy Goal Programming

  • Khalili-Damghani, Kaveh;Shahrokh, Ayda
    • Industrial Engineering and Management Systems
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    • 제13권4호
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    • pp.369-382
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    • 2014
  • This paper introduces a new multi-product multi-period multi-objective aggregate production planning problem. The proposed problem is modeled using multi-objective mixed-integer mathematical programming. Three objective functions, including minimizing total cost, maximizing customer services level, and maximizing the quality of end-product, are considered, simultaneously. Several constraints such as quantity of production, available time, work force levels, inventory levels, backordering levels, machine capacity, warehouse space and available budget are also considered. Some parameters of the proposed model are assumed to be qualitative and modeled using fuzzy sets. Then, a fuzzy goal programming approach is proposed to solve the model. The proposed approach is applied on a real-world industrial case study of a color and resin production company called Teiph-Saipa. The approach is coded using LINGO software. The efficacy and applicability of the proposed approach are illustrated in the case study. The results of proposed approach are compared with those of the existing experimental methods used in the company. The relative dominance of the proposed approach is revealed in comparison with the experimental method. Finally, a data dictionary, including the way of gathering data for running the model, is proposed in order to facilitate the re-implementation of the model for future development and case studies.

퍼지 PI 제어기를 이용한 풍력/디젤 하이브리드 발전시스템의 품질제어 (Power Quality Control of Wind/Diesel Hybrid Power Systems Using Fuzzy PI Controller)

  • 양수형;고정민;부창진;강민제;김정욱;김호찬
    • 한국태양에너지학회 논문집
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    • 제32권5호
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    • pp.1-10
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    • 2012
  • This paper proposes a modeling and controller design approach for a wind-diesel hybrid system including dump load. Wind turbine depends on nature such as wind speed. It causes power fluctuations of wind turbine. Excessive power fluctuation at stand-alone power grid is even worse than large-scale power grid. The proposed control scheme for power quality is fuzzy PI controller. This controller has advantages of PI and fuzzy controller. The proposed model is carried out by using Matlab/Simulink simulation program. In the simulation study, the proposed controller is compared with a conventional PI controller. Simulation results show that the proposed controller is more effective against disturbances caused by wind speed and load variation than the PI controller, and thus it contributes to a better quality wind-diesel hybrid power system.

Neuro-Fuzzy modeling of torsional strength of RC beams

  • Cevik, A.;Arslan, M.H.;Saracoglu, R.
    • Computers and Concrete
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    • 제9권6호
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    • pp.469-486
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    • 2012
  • This paper presents Neuro-Fuzzy (NF) based empirical modelling of torsional strength of RC beams for the first time in literature. The proposed model is based on fuzzy rules. The experimental database used for NF modelling is collected from the literature consisting of 76 RC beam tests. The input variables in the developed rule based on NF model are cross-sectional area of beams, dimensions of closed stirrups, spacing of stirrups, cross-sectional area of one-leg of closed stirrup, yield strength of stirrup and longitudinal reinforcement, steel ratio of stirrups, steel ratio of longitudinal reinforcement and concrete compressive strength. According to the selected variables, the formulated NFs were trained by using 60 of the 76 sample beams. Then, the method was tested with the other 16 sample beams. The accuracy rates were found to be about 96% for total set. The performance of accuracy of proposed NF model is furthermore compared with existing design codes by using the same database and found to be by far more accurate. The use of NF provided an alternative way for estimating the torsional strength of RC beams. The outcomes of this study are quite satisfactory which may serve NF approach to be widely used in further applications in the field of reinforced concrete structures.

Predicting the buckling load of smart multilayer columns using soft computing tools

  • Shahbazi, Yaser;Delavari, Ehsan;Chenaghlou, Mohammad Reza
    • Smart Structures and Systems
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    • 제13권1호
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    • pp.81-98
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    • 2014
  • This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.

상수처리시스템 응집제 주입공정 퍼지 모델링과 제어 (Fuzzy modeling and control for coagulant dosing process in water purification system)

  • 이수범;남의석;이봉국
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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    • pp.282-285
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    • 1996
  • In the water purification plant, the raw water is promptly purified by injecting chemicals. The amount of chemicals is directly related to water quality such as turbidity, temperature, pH and alkalinity. At present, however, the process of chemical reaction to the turbidity has not been clarified as yet. Since the process of coagulant dosage has no feedback signal, the amount of chemical can not be calculated from water quality data which were sensed from the plant. Accordingly, it has to be judged and determined by Jar-Test data which were made by skilled operators. In this paper, it is concerned to model and control the coagulant dosing process using jar-test results in order to predict optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride). The considering relations to the reaction of coagulation and flocculation, the five independent variables(turbidity, temperature, pH, Alkalinity of the raw water, PAC feed rate) are selected out and they are put into calculation to develope a neural network model and a fuzzy model for coagulant dosing process in water purification system. These model are utilized to predict optimum coagulant dosage which can minimize the water turbidity in flocculator. The efficacy of the proposed control schemes was examined by the field test.

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퍼지 수학적 형태학을 이용한 미세균열 모델링 (Modeling of Fine Cracks using Fuzzy Mathematical Morphology)

  • 박인규;최규석
    • 한국인터넷방송통신학회논문지
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    • 제10권5호
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    • pp.105-111
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    • 2010
  • 본 논문에서는 결함추출 알고리즘을 퍼지이론을 적용하여 유연성을 제시하고 실험을 통하여 타당성을 확인 하였다. 균열의 검출츨 위하여 퍼지형태학을 이용한 기본 연산자를 정의하였다. 이 연산자를 기초로 하여 퍼지집합에 대해 ${\lambda}$-퍼지척도를 이용하여 무게중심을 구하여 결함을 추출하였다.. 결함추출 알고리즘의 경우 양호한 결과를 낼 수 있었다. 하지만 균열이 매우 미세한 경우 영상 획득부분에 해당하는 조명장치를 얼마나 잘 설계하느냐가 중요하였다. 소프트웨어적으로는 룩업테이블을 사용하여 메모리의 사용을 최소화 시켜 좀 더 빠른 검사속도를 꾀하였다.

Design Fuzzy Controller for the Ball Positioning System Based on the Knowledge Acquisition and Adaptation

  • Hyeon Bae;Jung, Jae-Ryong;Kim, Sungshin
    • 한국지능시스템학회논문지
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    • 제11권7호
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    • pp.603-610
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    • 2001
  • Industrial processes are normally operated by skilled humans who have the cumulative and logical information about the system. Fuzzy control has been investigated for many application. Intelligent control approaches based on fuzzy logic have a chance to include human thinking. This paper represents modeling approach based upon operators knowledge without mathematical model of the system and optimize the controller. The experimented system is constructed for sending a ball to the goal position using wind of two DC motors in the predefined path. A vision camera to mimic human eyes detects the ball position. The system used in this experiment could be hardly modeled by mathematical methods and ould not be easily controlled by conventional manners. The controller is designed based on the input-output data and experimental knowledge obtained by trials, and optimized under the predefined performance criterion. And this paper shows the data adaptation for changeable operating condition. When the system is driven in the abnormal condition with unconsidered noise, the new optimal operating parameters could be defined by adjusting membership functions. Thus, this technique could be applied in industrial fields.

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